Evaluating Onset Times of Acoustic Emission Signals Using Histogram Distances

Determining the onset of a transient signal, for example in seismograms, acoustic emission (AE) signals, or ultrasonic signals is very important in non-destructive process monitoring and geophysics. In some cases, the AE from a malfunction is relatively weak, with a low signal-to-noise ratio. Thus, the signals are often hidden, making it rather difficult to separate them from the background noise. The present work proposes a new method of evaluating onset times based on bin-to-bin histogram distances measured with the Bhattacharyya coefficient. A criterion and a standard procedure for determining an onset are formulated. A key parameter, window length, was discussed in detail. Tests on AE signals from a pencil-lead break, single-grit scratching, and filament breakage reveal the feasibility and effectiveness of the proposed method. This method is believed to be especially appropriate when the emission signal strength of the target malfunction is lower than environmental AE signals generated by other stationary sources. It can also be used as an alternative method for identifying onsets in AE signals in process monitoring and other fields.

[1]  Yifan Zhao,et al.  Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals , 2017 .

[2]  John C Duke,et al.  Identifying the arrival of extensional and flexural wave modes using wavelet decomposition of ultrasonic signals , 2018, Ultrasonics.

[3]  Adam Glowacz,et al.  Fault diagnosis of single-phase induction motor based on acoustic signals , 2019, Mechanical Systems and Signal Processing.

[4]  Thomas Vogel,et al.  Acoustic emission for monitoring a reinforced concrete beam subject to four-point-bending , 2007 .

[5]  Paul Ziehl,et al.  Acoustic emission Bayesian source location: Onset time challenge , 2019, Mechanical Systems and Signal Processing.

[6]  Li Jin,et al.  A novel feature representation method based on original waveforms for acoustic emission signals , 2020 .

[7]  Miguel Delgado Prieto,et al.  Chromatic Monitoring of Gear Mechanical Degradation Based on Acoustic Emission , 2017, IEEE Transactions on Industrial Electronics.

[8]  Pengjian Shang,et al.  Extended AIC model based on high order moments and its application in the financial market , 2018, Physica A: Statistical Mechanics and its Applications.

[9]  A. Bhattacharyya On a measure of divergence between two statistical populations defined by their probability distributions , 1943 .

[10]  C. Leung,et al.  A new power-based method to determine the first arrival information of an acoustic emission wave , 2018, Structural Health Monitoring.

[11]  Giuseppe Lacidogna,et al.  Reliable onset time determination and source location of acoustic emissions in concrete structures , 2012 .

[12]  Reinoud Sleeman,et al.  Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings , 1999 .

[13]  Paul Ziehl,et al.  Hsu-Nielsen source acoustic emission data on a concrete block , 2019, Data in brief.

[14]  Manfred Baer,et al.  An automatic phase picker for local and teleseismic events , 1987 .

[15]  Longjun Dong,et al.  An Improved P-Phase Arrival Picking Method S/L-K-A with an Application to the Yongshaba Mine in China , 2018, Pure and Applied Geophysics.

[16]  Josef Sikula,et al.  New automatic localization technique of acoustic emission signals in thin metal plates. , 2009, Ultrasonics.

[17]  Jochen H Kurz,et al.  Strategies for reliable automatic onset time picking of acoustic emissions and of ultrasound signals in concrete. , 2005, Ultrasonics.

[18]  Olivier Romain,et al.  An accurate HMM-based similarity measure between finite sets of histograms , 2018, Pattern Analysis and Applications.

[19]  E. García Plaza,et al.  Abrasive Feature Related Acoustic Emission in Grinding , 2019, 2019 25th International Conference on Automation and Computing (ICAC).

[20]  James Hensman,et al.  A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding aerospace environments: An overview , 2017 .

[21]  N. Maeda A Method for Reading and Checking Phase Time in Auto-Processing System of Seismic Wave Data , 1985 .

[22]  Jiaqi Gong,et al.  Automatic time picking of microseismic data based on shearlet-AIC algorithm , 2018, Journal of Seismology.